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Meta-Learning for Query Conceptualization at Web Scale

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Meta-Learning for Query Conceptualization at Web Scale Applied Data Science Track Paper KDD '20, August 23–27, 2020, Virtual Event, USA Meta-Learning for Query Conceptualization at Web Scale Fred X. Han Di Niu Haolan Chen University of Alberta University of Alberta Tencent Edmonton, Canada Edmonton, Canada ShenZhen, China [email protected] [email protected] [email protected] Weidong Guo Shengli Yan Bowei Long Tencent Tencent Tencent BeiJing, China ShenZhen, China ShenZhen, China [email protected] [email protected] [email protected] ABSTRACT CCS CONCEPTS Concepts naturally constitute an abstraction for fine-grained enti- • Information systems ! Query representation; Query log ties and knowledge in the open domain. They enable search engines analysis; Query suggestion. and recommendation systems to enhance user experience by discov- ering high-level abstraction of a search query and the user intent KEYWORDS behind it. In this paper, we study the problem of query conceptual- Information retrieval; query analysis; conceptualization; meta-learning ization, which is to find the most appropriate matching concepts for any given search query from a large pool of pre-defined con- ACM Reference Format: cepts. We propose a coarse-to-fine approach to first reduce the Fred X. Han, Di Niu, Haolan Chen, Weidong Guo, Shengli Yan, and Bowei Long. 2020. Meta-Learning for Query Conceptualization at Web Scale. In search space for each query through a shortlisting scheme and then Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and identify the matching concepts using pre-trained language models, Data Mining (KDD ’20), August 23–27, 2020, Virtual Event, CA, USA. ACM, which are meta-tuned to our query-concept matching task. Our New York, NY, USA, 10 pages. https://doi.org/10.1145/3394486.3403357 shortlisting scheme involves using a GRU-based Relevant Words Generator (RWG) to first expand and complete the context of the 1 INTRODUCTION given query and then shortlisting the candidate concepts through a scoring mechanism based on word overlaps. To accurately identify Teaching machines to understand search queries and interpret the the most appropriate matching concepts for a query, even when user intents behind them is essential to building a more intelligent the concepts may have zero verbatim overlaps with the query, we web. To fully “understand” a search query means that a system is not meta-fine-tune a BERT pairwise text-matching model under the only capable of acquiring relevant knowledge about the terms in the Reptile meta-learning algorithm, which achieves zero-shot transfer query but is able to extrapolate beyond these terms to discover high- learning on the conceptualization problem. Our two-stage frame- level user intents, which often reveal additional hidden interests of work can be trained with data completely derived from a search the user. Query understanding is thus of profound significance to click graph, without requiring any human labelling efforts. For many downstream applications such as content recommendation, evaluation, we have constructed a large click graph based on more intent classification, and user-profiling. than 7 million instances of the click history recorded in Tencent QQ In this paper, we study the problem of search query conceptual- browser and performed the query conceptualization task based on a ization, which is to find one or multiple matching concepts fora large ontology with 159; 148 unique concepts. Results from a range given query from a large pool of pre-defined concepts. A concept of evaluation methods, including an offline evaluation procedure is a short text sequence that could be a phrase or a sentence. It on the click graph, human evaluation, online A/B testing and case associates various text entities under the isA relation. For example, studies, have demonstrated the superiority of our approach over a Toyota RAV4 isA real world entity under the concept fuel-efficient number of competitive pre-trained language models and fine-tuned SUV. The major benefit of query conceptualization is that concepts neural network baselines. create a tractable abstraction for the fine-grained knowledge in queries from the open domain. We showcase how conceptualization may help query understand- ing through an example in Fig. 1, where the task is to recommend queries after a user finishes reviewing a page of search results. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed We present the top-3 related queries for the input query Toyota for profit or commercial advantage and that copies bear this notice and the full citation RAV4 recommended by Google, Yahoo and Bing. Most of the rec- on the first page. Copyrights for components of this work owned by others than ACM ommended queries are more detailed versions of the original query. must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a While these can certainly be helpful to the user, in this paper, we are fee. Request permissions from [email protected]. interested in a different recommendation task, which is to discover KDD ’20, August 23–27, 2020, Virtual Event, CA, USA queries that have fewer or even no common words with the input © 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7998-4/20/08...$15.00 query but may characterize the query on a higher conceptual level https://doi.org/10.1145/3394486.3403357 (e.g., fuel-efficient SUVs). The ability of conceptualization, although 3064 Applied Data Science Track Paper KDD '20, August 23–27, 2020, Virtual Event, USA native to human intelligence, is what most search engines currently presents the experiment setup and results, while we discuss their lack and can benefit from. implications and conduct further analysis in Sec. 5. Finally, we We define that a concept is a match to a search query only ifthere review related work in Sec. 6 and conclude the work in Sec. 7. exists an isA relation between them, i.e., the knowledge conveyed in the query isA instance of the concept. Prior research on structured 2 FRAMEWORK knowledge base and taxonomy construction, such as DBPedia [3], We begin by formalizing the learning objective. Suppose that we YAGO [35], Probase [43], ConcepT [22], have provided abundant are given a fixed set C consisting of m unique concepts, i.e., C = sources to create the pool of concepts. fc1; c2; :::cm g. For a search query q, assume that there exists a non- We propose an efficient solution to the query conceptualization q q empty set Ct ⊂ C, where each concept in Ct is a match to q. Our problem based on deep neural networks and meta-learning, which q goal is to learn a model f to predict Ct for every query with the can effectively learn generalizable knowledge from a large amount following objective, of unlabelled click logs (i.e., the click graph) and transfer it to the Õ Õ query-concept matching problem. Our approach follows a coarse-to- max log p¹ci jq;C; f º; (1) f q 2Q q fine strategy in a two-stage framework. Specifically, we introduce a ci 2Ct reliable shortlisting scheme to reduce the search space for concepts, where Q represents the set of search queries available for training. where a GRU-based Relevant Words Generator (RWG) takes in a The dependence on C indicates that the cost of learning is pro- search query and produces the most relevant concept words, which portional to m. Since we lack a large amount of labelled training helps to reveal the its surrounding context. query-concept pairs, supervised classification is not feasible when To narrow the shortlist down to the most appropriate match- m is large. A shortlisting mechanism is thus needed to reduce the ing concepts, in the second stage, we meta-train a pairwise neural concept search space for each query. text-matching model, where each candidate concept is matched Let us define a shortlisting model д, which takes in q and the against the input query, and the result indicates if there is indeed q complete concept set C, and then outputs a new set C ⊂ C. The an isA relation among the pair. The main challenge here is that it s purpose of д is to constrain the search space for each query from is impossible to manually label an unbiased training set due to the q C to Cs . The original objective from Eq. 1 then becomes more large number of candidate concepts at the web scale. Realizing that q tractable since jC j ≪ jCj, and it is expressed as many concepts are present in and mined from the click graph (i.e., s Õ Õ q most concepts are a part of a searched query or document title), we max log p¹ci jq;Cs ; f º: (2) f q 2Q q believe the problem of query-concept matching shares the same un- ci 2Ct derlying distribution with other similar natural language matching To transform Eq. 2 into a text-matching problem, we define the tasks sampled from the click graph, which naturally links to the idea matching degree between a concept and a query as r, where r is a of meta-learning [9, 25]. Therefore, we derive four text-matching binary label of either 0 or 1, and the objective becomes tasks by mining the click graph and utilize Reptile [25], which is Õ Õ a type of Model-Agnostic Meta-Learning (MAML) algorithm, to max log p¹rq;ci jq; ci ; f º; (3) f q 2Q q meta-tune a BERT [7] model on these tasks.
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